{"id":"W7083582979","doi":"10.1016/j.compeleceng.2025.110714","title":"Multi-objective optimization of nanogrids for remote telecom base stations in Canada","year":2025,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Municipal Solid Waste Management","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"École Centrale de Lyon; Région Hauts-de-France; Institut National des Sciences Appliquées de Lyon; Centre National de la Recherche Scientifique; Fonds de recherche du Québec – Nature et technologies; Université Grenoble Alpes; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke; Canada Research Chairs; Indian National Science Academy","keywords":"Base station; Renewable energy; Diesel fuel; Snow; Limiting; Snow removal; Minification; Benchmark (surveying)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008428987,0.00009222996,0.000140896,0.0001053487,0.00002805596,0.000007645624,0.0001663701,0.00002205295,0.00001102105],"category_scores_gemma":[0.00006926367,0.0001040455,0.00002697875,0.000714443,0.00000944176,0.00005090925,0.00009335356,0.00008231014,5.13472e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001070149,"about_ca_system_score_gemma":0.00006656929,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1515161,"about_ca_topic_score_gemma":0.06718228,"domain_scores_codex":[0.9992542,0.00001538185,0.000211823,0.0001800684,0.0001071908,0.0002313887],"domain_scores_gemma":[0.9996376,0.0001513933,0.00003429907,0.0001263692,0.000009284818,0.0000410675],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005761649,0.00002054798,0.0004624816,0.00001573916,0.00001018254,0.000001315115,0.00003541682,0.9895699,0.0001696003,0.00006444048,0.0002769709,0.009367664],"study_design_scores_gemma":[0.0003844956,0.0000235347,0.007285224,0.00002345432,0.000006867298,1.970132e-7,0.000009712828,0.9914204,0.0003805357,0.000007499724,0.0003698244,0.00008829051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02201738,0.00003057023,0.9772521,0.0000790198,0.0001297797,0.0003507837,0.000002903916,0.00001687109,0.0001205838],"genre_scores_gemma":[0.8039804,0.000007775443,0.1958399,0.00009365139,0.000006339096,0.00001520442,0.000007167751,0.000009342381,0.00004022838],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.781963,"threshold_uncertainty_score":0.9498392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005849329495814765,"score_gpt":0.2047172476567223,"score_spread":0.1988679181609075,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}